AI RESEARCH
From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection
arXiv CS.CV
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ArXi:2604.09327v1 Announce Type: new Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations. However, traditional frame-level evaluations treat video as a collection of isolated frames, fundamentally misaligned with how anomalies manifest and are acted upon in the real world.